Data Allocation for Embedded Systems with Hybrid On-Chip Scratchpad and Caches

Abstract

The memory subsystem is the performance bottleneck for data intensive applications, which makes it a key consideration in high-performance embedded system optimization. Onchip SRAMs including scratchpad memories (SPMs) and caches are widely used in embedded systems to narrow the speed gap between CPU and memory. However, many existing SPM data allocation algorithms are designed for architectures with pure SPM as on-chip SRAM. As a result, for off-the-shelf embedded processors with hybrid on-chip scratchpad and caches, these algorithms may not lead to an optimal overall performance due to the lack of consideration for cache behaviors. In this paper, we propose an comprehensive data allocation framework for the above-mentioned architectures. We formulate a cache-aware integer linear programming (ILP) model to identify possible memory objects to be allocated into SPM for average case execution time improvement. The impact of SPM allocation on cache interferences are captured to reduce the overall number of slow off-chip memory accesses. Experimental results show that such a hybrid on-chip SRAM organization outperforms the pure cache or SPM architecture with our data allocation mechanism. We evaluate the execution cycles via selecting dataintensive benchmarks for different SPM-cache size combinations, where up to 25.4% total execution cycle reduction is achieved.